The replica is a Sidewalk Labs spinoff firm that uses the power of data to make complicated, quickly changing urban landscapes easier to understand – data that can inform city planning in significantly more responsive ways than it presently does.
“You wouldn’t find any mention of Uber, Lyft, or ride-sharing, or Amazon, or Prime, or any number of things if you went back and looked at any city-wide or regional-wide comprehensive plan from the year 2000,” says Replica CEO Nick Bowden. “Just that tiny list of five things has changed the way cities run fundamentally. So, there’s this obvious: ‘Wait a minute!’ We don’t have the flexibility or adaptability to keep up with the tremendous pace of innovation in the built environment.’

Replica works with public-sector agencies across the United States to give long-term planners with that flexibility. Bowden discussed Covid’s enormous impact on urban systems with Sidewalk Talk editors Eric Jaffe and Vanessa Quirk, as well as how Replica incorporates privacy into its methodology and how the firm engages with governments to help them predict a greener, more liveable future.

Above is a video of our talk; below is an edited transcript.

Eric Jaffe (Eric Jaffe): Can you explain what Replica does and what its mission is to individuals who aren’t familiar with it?

Nick Bowden (Nick): We refer to ourselves as a data platform for the built environment, and we use that word quite often. We give information on mobility (how, where, and why people move), economic activity (where, how, and why people spend money), and land use (how is land use changing, how is the land being used in a particular city). Then there are other measures for topics like public health. How does the presence of Covid alter things? How do we deliver data that gives insight into the system that is a city, or the system that is a region, rather than simply a very narrow collection of data around mobility or a very focused set of data around economic activity, is how I would describe a lot of our objective.

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Vanessa Quirk (Vanessa): Can you explain how government agencies have typically derived long-term planning insights, the limitations of those old methodologies, and how Replica advances those efforts?

I began my work in the public sector, so I’ve had firsthand experience with this. Historically, the business model, if you want to use that word, has been to estimate the distant future using what we would call “long ago data.” Many planning processes start with the most recent Census, which could be three, four, five, or ten years old, and then extrapolate forward 25 or 30 years. I’m not sure if there’s anything fundamentally wrong with that approach, but the obvious flaw is that it doesn’t address much of what’s going on right now.

If you went back and looked at any city-wide or regional-wide comprehensive plan from the year 2000, which was frequently aiming at a 2030 or 2040 type of horizon, you wouldn’t find any mention of Uber, Lyft, or ride sharing, or Amazon, or Prime, or any number of other things. Even that little list of five items has had a significant impact on how cities operate. So, for example, there’s the obvious: “Wait a second, we don’t have the flexibility or adaptability to adjust to the kind of quickly changing innovation that’s happened in the built world.”

That’s a rough summary of how things function nowadays. The world is probably changing quicker than it has ever been, and cities have never been fundamentally in a position to adjust swiftly to those changes. The most extreme example of this is Covid. Overnight, ridership on public transportation plummets by about 80%. There are just no workflows or systems in place to accommodate this.

From a company or product standpoint, some of what we’re trying to do is actually compress those time horizons in, and say, “What if we looked at last quarter, last month, or last week?” And, rather than thinking about it in 25-, 30-, or 40-year intervals, we think about it in one-, two-, or five-year increments to allow for flexibility.

Vanessa Quirk (Vanessa): It’s critical to underline that you’re looking at demographic data rather than personal information. Could you elaborate on how you think about maintaining privacy in your work?

It’s an excellent question. Privacy, in my opinion, is at the heart of all we do. To provide some background, I’d say that in the public sector, there has always been a tradeoff: do we want high-fidelity data that comes with major privacy risk, or do we want lower-fidelity data that comes with no privacy risk?

In the present world of data and computing, that appears to be a false tradeoff. We, like our public-sector clients, are never concerned in the mobility of a single person. Joe and Mary aren’t the focus of a policy framework. You create a policy framework to govern how the system works, and it all originates from this systems perspective. Individual data has relatively little incentive and/or justification to exist. It just doesn’t seem to make any sense.

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A big part of our strategy has been developed to collect and interpret a vast composite of input data sources, train a set of behaviour models that replicate what real people do, and then represent that in a model that does not compromise fidelity for privacy. And all of the use cases involve groups of individuals. So, how is this particular transit line used by a group of people? What is the best way for a group of individuals to use this portion of town? What does a group of people do with their money?

I’m happy you inquired. It’s a crucial component of our strategy. From a technical standpoint, I believe it has never been conceivable in the past.

Eric Jaffe (Eric Jaffe): Let’s look at one of such scenarios. I know you work in California, which just became the first state to adopt a new measure for measuring the impact of new construction on the transportation system called vehicle miles travelled. Can you talk about that work and what it takes to work with the government directly?

California has a long history of being one of the most progressive states in the US, pushing the envelope on important policy, regulatory, and even financial parameters. They just converted from a framework based on “level of service” — effectively, “level of service” is a measure of congestion — to one based on vehicle miles travelled [VMT] — that is, how many miles does a person accrue over the course of a day? So, whether you go from home to work, or from work to school, or from lunch to dinner, or from dinner to bed, that whole measurement is taken. VMT has been linked to emissions and climate change through extensive research.